In [1]:
import os
import numpy as np
import pandas as pd
import imageio
import matplotlib as mpl
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
from PIL import Image, ImageOps
import scipy.ndimage as ndi
from keras.models import Sequential
from keras.layers import Dense, Flatten, Conv2D, MaxPool2D, Dropout, Activation, BatchNormalization
from keras.preprocessing.image import ImageDataGenerator
from keras.callbacks import ReduceLROnPlateau
from keras.preprocessing import image
from keras.utils import plot_model
In [2]:
dirname = 'C:/Users/ANUBHAV/Documents/chest-xray-pneumonia'
train_nrml_pth = 'C:/Users/ANUBHAV/Documents/chest-xray-pneumonia/train/NORMAL/'
train_pnm_pth = 'C:/Users/ANUBHAV/Documents/chest-xray-pneumonia/train/PNEUMONIA/'
test_nrml_pth = 'C:/Users/ANUBHAV/Documents/chest-xray-pneumonia/test/NORMAL/'
test_pnm_pth ='C:/Users/ANUBHAV/Documents/chest-xray-pneumonia/test/PNEUMONIA/'
val_nrml_pth ='C:/Users/ANUBHAV/Documents/chest-xray-pneumonia/val/NORMAL/'
val_pnm_pth = 'C:/Users/ANUBHAV/Documents/chest-xray-pneumonia/val/PNEUMONIA/'
In [3]:
def plot_imgs(item_dir, num_imgs=25):
all_item_dirs = os.listdir(item_dir)
item_files = [os.path.join(item_dir, file) for file in all_item_dirs][:num_imgs]
plt.figure(figsize=(10, 10))
for idx, img_path in enumerate(item_files):
plt.subplot(5, 5, idx+1)
img = plt.imread(img_path)
plt.imshow(img)
plt.tight_layout()
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plot_imgs(train_nrml_pth)
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plot_imgs(train_pnm_pth)
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def plot_img_hist(item_dir, num_img=6):
all_item_dirs = os.listdir(item_dir)
item_files = [os.path.join(item_dir, file) for file in all_item_dirs][:num_img]
#plt.figure(figsize=(10, 10))
for idx, img_path in enumerate(item_files):
fig1 = plt.figure(idx,figsize=(10, 10))
fig1.add_subplot(2, 2, 1)
img = mpimg.imread(img_path, )
plt.imshow(img)
fig1.add_subplot(2, 2, 2)
plt.hist(img.ravel(),bins=256, fc='k', ec='k')
plt.tight_layout()
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plot_img_hist(train_pnm_pth,2)
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def plot_img_hist_ndi(item_dir, num_img=6):
all_item_dirs = os.listdir(item_dir)
item_files = [os.path.join(item_dir, file) for file in all_item_dirs][:num_img]
#plt.figure(figsize=(10, 10))
for idx, img_path in enumerate(item_files):
im = imageio.imread(img_path)
hist = ndi.histogram(im, min=0, max=255, bins=256)
cdf = hist.cumsum() / hist.sum()
fig1 = plt.figure(idx,figsize=(10, 10))
fig1.add_subplot(2, 3, 1)
img = mpimg.imread(img_path, )
plt.title("No. {}".format(idx))
plt.imshow(img)
fig1.add_subplot(2, 3, 2)
plt.title("Histogram")
plt.plot(hist)
fig1.add_subplot(2, 3, 3)
plt.title("CDF")
plt.plot(cdf)
plt.tight_layout()
plot_img_hist_ndi(train_pnm_pth,2)
In [9]:
train_nrml_pth_work = 'C:/Users/ANUBHAV/Documents/chest-xray-pneumonia/train/NORMAL/'
train_pnm_pth_work = 'C:/Users/ANUBHAV/Documents/chest-xray-pneumonia/train/PNEUMONIA/'
test_nrml_pth_work ='C:/Users/ANUBHAV/Documents/chest-xray-pneumonia/test/NORMAL/'
test_pnm_pth_work = 'C:/Users/ANUBHAV/Documents/chest-xray-pneumonia/test/PNEUMONIA/'
In [10]:
def image_resizing(path_from, path_to, height=500, width=500):
size = height, width
i=1
files = os.listdir(path_from)
for file in files:
try:
file_dir = os.path.join(path_from, file)
file_dir_save = os.path.join(path_to, file)
img = Image.open(file_dir)
img = img.resize(size, Image.ANTIALIAS)
img = img.convert("RGB")
img.save(file_dir_save)
i=i+1
except:
continue
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image_resizing(train_nrml_pth, train_nrml_pth_work, 300, 300)
image_resizing(train_pnm_pth, train_pnm_pth_work, 300, 300)
image_resizing(test_nrml_pth, test_nrml_pth_work, 300, 300)
image_resizing(test_pnm_pth, test_pnm_pth_work, 300, 300)
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plot_imgs(train_nrml_pth_work)
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plot_imgs(train_pnm_pth_work)
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def hist_equal(path_from, path_to):
i=1
files = os.listdir(path_from)
for file in files:
try:
file_dir = os.path.join(path_from, file)
file_dir_save = os.path.join(path_to, file)
img = Image.open(file_dir)
img = ImageOps.equalize(img)
#img = img.convert("RGB") #konwersja z RGBA do RGB, usuniecie kanału alfa zeby zapisać do jpg
img.save(file_dir_save)
i=i+1
except:
continue
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hist_equal(train_pnm_pth_work, train_pnm_pth_work)
hist_equal(train_nrml_pth_work, train_nrml_pth_work)
hist_equal(test_pnm_pth_work, test_pnm_pth_work)
hist_equal(test_nrml_pth_work, test_nrml_pth_work)
plot_img_hist(train_pnm_pth_work, 2)
In [16]:
plot_img_hist_ndi(train_pnm_pth_work, 2)
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img_size_h = 300
img_size_w = 300
input_shape = (img_size_h, img_size_w, 1)
model = Sequential([
Conv2D(32, (3,3), input_shape=input_shape),
MaxPool2D((2, 2)),
Conv2D(32, (3,3)),
MaxPool2D((2, 2)),
Conv2D(64, (3,3)),
MaxPool2D((2, 2)),
Conv2D(64, (3,3)),
MaxPool2D((2, 2)),
Flatten(),
Dense(128, activation='relu'),
Dropout(0.5),
Dense(1, activation='sigmoid')
])
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
model.summary()
In [18]:
train_path_work='C:/Users/ANUBHAV/Documents/chest-xray-pneumonia/train/'
test_path_work='C:/Users/ANUBHAV/Documents/chest-xray-pneumonia/test/'
In [19]:
train_datagen = ImageDataGenerator(
rescale=1./255,
featurewise_center=True,
featurewise_std_normalization=True,
rotation_range=45,
width_shift_range=0.5,
height_shift_range=0.5,
shear_range=0.2,
zoom_range=0.2,
horizontal_flip=True)
val_datagen = ImageDataGenerator(
rescale=1./255,
rotation_range=45,
width_shift_range=0.5,
height_shift_range=0.5,
shear_range=0.2,
zoom_range=0.2,
horizontal_flip=True)
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batch_size = 32
train_generator = train_datagen.flow_from_directory(
train_path_work,
target_size=(img_size_h, img_size_w),
color_mode='grayscale', #we use grayscale images I think
batch_size=batch_size,
class_mode='binary',
shuffle=True, #we shuffle our images for better performance
seed=8)
validation_generator = val_datagen.flow_from_directory(
test_path_work,
target_size=(img_size_h, img_size_w),
color_mode='grayscale',
batch_size=batch_size,
class_mode='binary',
shuffle=True,
seed=8)
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training_examples = 5216
validation_examples = 624
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learning_rate_reduction = ReduceLROnPlateau(monitor='val_accuracy',
patience=2,
verbose=1,
factor=0.5,
min_lr=0.0001) #0.00001
callback = [learning_rate_reduction]
In [23]:
history = model.fit_generator(
train_generator,
epochs=20,
validation_data=validation_generator,
callbacks = callback
)
In [24]:
plt.plot(history.history['accuracy'])
plt.plot(history.history['val_accuracy'])
plt.title('Model accuracy')
plt.ylabel('Accuracy')
plt.xlabel('Epoch')
plt.legend(['Train', 'Test'], loc='upper left')
plt.show()
# Plot training & validation loss values
plt.plot(history.history['loss'])
plt.plot(history.history['val_loss'])
plt.title('Model loss')
plt.ylabel('Loss')
plt.xlabel('Epoch')
plt.legend(['Train', 'Test'], loc='upper left')
plt.show()
In [25]:
img_test_path = os.path.join(test_nrml_pth, 'NORMAL2-IM-0337-0001.jpeg')
img_train_path_ill = os.path.join(train_pnm_pth, 'person1787_bacteria_4634.jpeg')
img_p = image.load_img(img_test_path, target_size=(img_size_h, img_size_w), color_mode='grayscale')
img_arr_p = np.array(img_p)
img_arr_p = np.expand_dims(img_arr_p, axis=0)
img_arr_p = np.expand_dims(img_arr_p, axis=3)
images_p = np.vstack([img_arr_p])
In [26]:
def predict_illness(image_path):
imge = plt.imread(image_path)
plt.imshow(imge)
img = image.load_img(image_path, target_size=(img_size_h, img_size_w), color_mode='grayscale')
x = image.img_to_array(img)
x = np.expand_dims(x, axis=0)
images = np.vstack([x])
classes = model.predict_classes(images, batch_size=10)
if classes[0][0] == 0:
print("They got healthy!")
else:
print("They got pneumonia!")
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predict_illness(img_train_path_ill)
In [28]:
from keras.models import Model
layer_outputs = [layer.output for layer in model.layers[:len(model.layers)]]
activation_model = Model(inputs=model.input, outputs=layer_outputs)
activations = activation_model.predict(images_p)
first_layer_activation = activations[0]
plt.matshow(first_layer_activation[0, :, :, 4], cmap='viridis')
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model.layers[:-1]# Droping The Last Dense Layer
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In [30]:
layer_names = []
for layer in model.layers[:-1]:
layer_names.append(layer.name)
images_per_row = 16
zipped_layers = zip(layer_names, activations)
for layer_name, layer_activation in zipped_layers: #this loop
if layer_name.startswith('conv'):
n_features = layer_activation.shape[-1]
size = layer_activation.shape[1]
n_cols = n_features // images_per_row
display_grid = np.zeros((size * n_cols, images_per_row * size))
for col in range(n_cols):
for row in range(images_per_row):
channel_image = layer_activation[0,:, :, col * images_per_row + row]
channel_image -= channel_image.mean()
channel_image /= channel_image.std()
channel_image *= 64
channel_image += 128
channel_image = np.clip(channel_image, 0, 255).astype('uint8')
display_grid[col * size : (col + 1) * size,
row * size : (row + 1) * size] = channel_image
scale = 1. / size
plt.figure(figsize=(scale * display_grid.shape[1],
scale * display_grid.shape[0]))
plt.title(layer_name)
plt.grid(False)
plt.imshow(display_grid, aspect='auto', cmap='viridis')
In [31]:
layer_names = []
for layer in model.layers[:-1]:
layer_names.append(layer.name)
images_per_row = 16
for layer_name, layer_activation in zip(layer_names, activations):
if layer_name.startswith('max'):
n_features = layer_activation.shape[-1]
size = layer_activation.shape[1]
n_cols = n_features // images_per_row
display_grid = np.zeros((size * n_cols, images_per_row * size))
for col in range(n_cols):
for row in range(images_per_row):
channel_image = layer_activation[0,:, :, col * images_per_row + row]
channel_image -= channel_image.mean()
channel_image /= channel_image.std()
channel_image *= 64
channel_image += 128
channel_image = np.clip(channel_image, 0, 255).astype('uint8')
display_grid[col * size : (col + 1) * size,
row * size : (row + 1) * size] = channel_image
scale = 1. / size
plt.figure(figsize=(scale * display_grid.shape[1],
scale * display_grid.shape[0]))
plt.title(layer_name)
plt.grid(False)
plt.imshow(display_grid, aspect='auto', cmap='viridis')
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